




"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 				UpperCAmelCase		(		snake_case      :       List[Any]   ,							snake_case      :       int   ,							snake_case      :       int   ,							snake_case      :       Dict   ,							snake_case      :       Tuple     ):
					# load base model
					_lowerCAmelCase:Any						=	StableDiffusionPipeline.from_pretrained(a_   ,							torch_dtype=torch.floataa     )

					# load LoRA weight from .safetensors
					_lowerCAmelCase:Optional[int]						=	load_file(a_     )

					_lowerCAmelCase:Tuple						=	[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															_lowerCAmelCase:List[str]						=	key.split('''.'''     )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''     )[-1].split('''_'''     )
															_lowerCAmelCase:str						=	pipeline.text_encoder
										else:
															_lowerCAmelCase:Any						=	key.split('''.'''     )[0].split(LORA_PREFIX_UNET + '''_'''     )[-1].split('''_'''     )
															_lowerCAmelCase:str						=	pipeline.unet

										# find the target layer
										_lowerCAmelCase:Optional[int]						=	layer_infos.pop(0     )
										while len(a_     ) > -1:
															try:
																				_lowerCAmelCase:Union[str, Any]						=	curr_layer.__getattr__(a_     )
																				if len(a_     ) > 0:
																									_lowerCAmelCase:List[Any]						=	layer_infos.pop(0     )
																				elif len(a_     ) == 0:
																									break
															except Exception:
																				if len(a_     ) > 0:
																									temp_name += "_" + layer_infos.pop(0     )
																				else:
																									_lowerCAmelCase:Optional[Any]						=	layer_infos.pop(0     )

										_lowerCAmelCase:Optional[Any]						=	[]
										if "lora_down" in key:
															pair_keys.append(key.replace('''lora_down'''   ,							'''lora_up'''     )     )
															pair_keys.append(a_     )
										else:
															pair_keys.append(a_     )
															pair_keys.append(key.replace('''lora_up'''   ,							'''lora_down'''     )     )

										# update weight
										if len(state_dict[pair_keys[0]].shape     ) == 4:
															_lowerCAmelCase:List[Any]						=	state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
															_lowerCAmelCase:Optional[int]						=	state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
															curr_layer.weight.data += alpha * torch.mm(a_   ,							a_     ).unsqueeze(2     ).unsqueeze(3     )
										else:
															_lowerCAmelCase:List[Any]						=	state_dict[pair_keys[0]].to(torch.floataa     )
															_lowerCAmelCase:Optional[Any]						=	state_dict[pair_keys[1]].to(torch.floataa     )
															curr_layer.weight.data += alpha * torch.mm(a_   ,							a_     )

										# update visited list
										for item in pair_keys:
															visited.append(a_     )

					return pipeline


if __name__ == "__main__":
				UpperCamelCase__    =      argparse.ArgumentParser()

				parser.add_argument(
				    '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
				)
				parser.add_argument(
				    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
				)
				parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				parser.add_argument(
				    '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
				)
				parser.add_argument(
				    '''--lora_prefix_text_encoder''',
				    default='''lora_te''',
				    type=str,
				    help='''The prefix of text encoder weight in safetensors''',
				)
				parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
				parser.add_argument(
				    '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
				)
				parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

				UpperCamelCase__    =      parser.parse_args()

				UpperCamelCase__    =      args.base_model_path
				UpperCamelCase__    =      args.checkpoint_path
				UpperCamelCase__    =      args.dump_path
				UpperCamelCase__    =      args.lora_prefix_unet
				UpperCamelCase__    =      args.lora_prefix_text_encoder
				UpperCamelCase__    =      args.alpha

				UpperCamelCase__    =      convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				UpperCamelCase__    =      pipe.to(args.device)
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)













'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



